human exacerbation data
Mingming Zhang & Amrit Singh
December 22, 2025
Setup: Libraries and utilities
import single cell data
data <- read_h5ad(here::here("data", "preprocessedData", "GSE193816", "GSE193816_all_cells_data.h5ad"))
data <- CreateSeuratObject(counts = t(as.matrix(data$X)),
meta.data = data$obs)
## mnp
mnp <- read_h5ad(here::here("data","preprocessedData", "GSE193816", "GSE193816_mnp_data.h5ad"))
mnp <- CreateSeuratObject(counts = t(as.matrix(mnp$X)),
meta.data = mnp$obs)
## t cells
tcell <- read_h5ad(here::here("data","preprocessedData", "GSE193816", "GSE193816_t_cell_data.h5ad"))
tcell <- CreateSeuratObject(counts = t(as.matrix(tcell$X)),
meta.data = tcell$obs)quality metrics
All cells
VlnPlot(data, features = c("nFeature_RNA", "nCount_RNA",
"percent_mito"), ncol = 5)
MNP cells
VlnPlot(mnp, features = c("nFeature_RNA", "nCount_RNA",
"percent_mito"), ncol = 5)
T cells
VlnPlot(tcell, features = c("nFeature_RNA", "nCount_RNA",
"percent_mito"), ncol = 5)
Dimension reduction plots
All cells
data <- NormalizeData(data,
normalization.method = "LogNormalize") %>%
FindVariableFeatures(selection.method = "vst",
nfeatures = 2000) %>%
ScaleData(features = rownames(data)) %>%
RunPCA() %>%
RunUMAP(dims = 1:50, reduction.name = 'umap.rna',
reduction.key = 'rnaUMAP_')
Idents(data) <- data@meta.data$cluster
allcells <- DimPlot(data, reduction = "umap.rna", label = TRUE,
label.size = 4, repel = TRUE, group.by = "cluster") +
ggtitle("RNA - all cells") +
xlab("UMAP 1") +
ylab("UMAP 2")
ggsave(here::here("results", "allcells_scrnaseq.png"), allcells, height = 4, width = 6)
allcells
save markers of all cells
res <- Seurat::FindAllMarkers(data, only.pos = TRUE)
cell_markers <- res %>%
group_by(cluster) %>%
dplyr::slice(1:10)
cellmarkers <- split(cell_markers$gene, cell_markers$cluster)
saveRDS(cellmarkers, here::here("results", "cellmarkers_GSE193816.rds"))
cellmarkers## $`B cells`
## [1] "MS4A1" "BANK1" "CD79A" "HLA-DQA1" "CD79B" "TNFRSF13C"
## [7] "HLA-DQB1" "IGKC" "POU2F2" "HLA-DPB1"
##
## $`CD4 T cells`
## [1] "LTB" "CD3D" "IL32" "TRAC" "IL7R" "CD2" "SPOCK2" "CD3E"
## [9] "CD3G" "IKZF1"
##
## $`CD8 T cells`
## [1] "CCL5" "CD3D" "CD2" "CD3G" "CD8A" "IL32" "TRAC" "NKG7" "CD3E" "GZMA"
##
## $`Epithelial cells`
## [1] "KRT19" "FXYD3" "AGR2" "WFDC2" "ALDH1A1" "PERP" "SLPI"
## [8] "KRT18" "CXCL17" "TACSTD2"
##
## $MNP
## [1] "TYROBP" "FCER1G" "AIF1" "HLA-DQB1" "HLA-DQA1" "LST1"
## [7] "HLA-DPB1" "CCDC88A" "MS4A6A" "IGSF6"
##
## $`Mast cells`
## [1] "GATA2" "CPA3" "SAMSN1" "AKAP12" "HDC" "IL1RL1" "MS4A2"
## [8] "CSF2RB" "SLC18A2" "SLC45A3"
##
## $`NK cells`
## [1] "TRDC" "XCL1" "TNFRSF18" "GNLY" "XCL2" "TXK"
## [7] "KLRF1" "LINC00996" "SPINK2" "SH2D1B"MNP cells
mnp <- NormalizeData(mnp,
normalization.method = "LogNormalize") %>%
FindVariableFeatures(selection.method = "vst",
nfeatures = 2000) %>%
ScaleData(features = rownames(mnp)) %>%
RunPCA() %>%
RunUMAP(dims = 1:50, reduction.name = 'umap.rna',
reduction.key = 'rnaUMAP_')
mnpcells <- DimPlot(mnp, reduction = "umap.rna", label = TRUE,
label.size = 2.5, repel = TRUE, group.by = "annotation") +
ggtitle("RNA - MNP cells") +
xlab("UMAP 1") +
ylab("UMAP 2")
ggsave(here::here("results", "mnpcells_scrnaseq.png"), mnpcells, height = 4, width = 6)
mnpcells
T cells
tcell <- NormalizeData(tcell,
normalization.method = "LogNormalize") %>%
FindVariableFeatures(selection.method = "vst",
nfeatures = 2000) %>%
ScaleData(features = rownames(tcell)) %>%
RunPCA() %>%
RunUMAP(dims = 1:50, reduction.name = 'umap.rna',
reduction.key = 'rnaUMAP_')
tcells <- DimPlot(tcell, reduction = "umap.rna", label = TRUE,
label.size = 2.5, repel = TRUE, group.by = "annotation") +
ggtitle("RNA - T cells") +
xlab("UMAP 1") +
ylab("UMAP 2")
ggsave(here::here("results", "tcells_scrnaseq.png"), tcells, height = 4, width = 6)
tcells
Cell-type proportions
All cells
data@meta.data %>%
dplyr::select(id, group, condition, cluster) %>%
dplyr::group_by(id, group, condition, cluster) %>%
mutate(n = n()) %>%
mutate(condition = factor(as.character(condition),
levels = c("Bln", "Dil", "Ag"))) %>%
ggplot(aes(x = condition, y = n, color = group)) +
geom_boxplot() +
facet_wrap(~cluster, scales = "free") +
theme_bw()
MNP cells
mnp@meta.data %>%
dplyr::select(id, group, condition, annotation) %>%
dplyr::group_by(id, group, condition, annotation) %>%
mutate(n = n()) %>%
mutate(condition = factor(as.character(condition),
levels = c("Bln", "Dil", "Ag"))) %>%
ggplot(aes(y = annotation, x = n, color = condition)) +
geom_boxplot() +
facet_grid(~group) +
theme_bw()
T cells
tcell@meta.data %>%
dplyr::select(id, group, condition, annotation) %>%
dplyr::group_by(id, group, condition, annotation) %>%
mutate(n = n()) %>%
mutate(condition = factor(as.character(condition),
levels = c("Bln", "Dil", "Ag"))) %>%
ggplot(aes(y = annotation, x = n, color = condition)) +
geom_boxplot() +
facet_grid(~group) +
theme_bw()
Differential expression analysis
All cells
sct_counts <- GetAssayData(data, assay = "RNA", layer = "counts")
metadata_aa <- data@meta.data %>%
dplyr::filter(group == "AA",
condition != "Dil")
metadata_aa$group_id <- droplevels(metadata_aa$condition)
metadata_aa$sample_id <- droplevels(metadata_aa$Channel)
all_aa = sc_diffexp(sct_counts, metadata_aa, cell_ann_col = "cluster")## | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100%all_aa$toptables$condition <- "AA"
all_aa$toptables$cc <- "All cells"
metadata_ac <- data@meta.data %>%
dplyr::filter(group == "AC",
condition != "Dil")
metadata_ac$group_id <- droplevels(metadata_ac$condition)
metadata_ac$sample_id <- droplevels(metadata_ac$Channel)
all_ac = sc_diffexp(sct_counts, metadata_ac, cell_ann_col = "cluster")## | | | 0% | |========== | 14% | |==================== | 29% | |============================== | 43% | |======================================== | 57% | |================================================== | 71% | |============================================================ | 86% | |======================================================================| 100%all_ac$toptables$condition <- "AC"
all_ac$toptables$cc <- "All cells"
saveRDS(all_aa$toptables,
here::here("results", "sc_exacer_aa_toptable.rds"))MNP cells
sct_counts <- GetAssayData(mnp, assay = "RNA", layer = "counts")
metadata_aa <- mnp@meta.data %>%
dplyr::filter(group == "AA",
condition != "Dil")
metadata_aa$group_id <- droplevels(metadata_aa$condition)
metadata_aa$sample_id <- droplevels(metadata_aa$Channel)
mnp_aa = sc_diffexp(sct_counts, metadata_aa, cell_ann_col = "annotation")## | | | 0% | |===== | 7% | |========== | 14% | |=============== | 21% | |==================== | 29% | |========================= | 36% | |============================== | 43% | |=================================== | 50% | |======================================== | 57% | |============================================= | 64% | |================================================== | 71% | |======================================================= | 79% | |============================================================ | 86% | |================================================================= | 93% | |======================================================================| 100%mnp_aa$toptables$condition <- "AA"
mnp_aa$toptables$cc <- "MNP cells"
metadata_ac <- mnp@meta.data %>%
dplyr::filter(group == "AC",
condition != "Dil")
metadata_ac$group_id <- droplevels(metadata_ac$condition)
metadata_ac$sample_id <- droplevels(metadata_ac$Channel)
mnp_ac = sc_diffexp(sct_counts, metadata_ac, cell_ann_col = "annotation")## | | | 0% | |===== | 7% | |========== | 14% | |=============== | 21% | |==================== | 29% | |========================= | 36% | |============================== | 43% | |=================================== | 50% | |======================================== | 57% | |============================================= | 64% | |================================================== | 71% | |======================================================= | 79% | |============================================================ | 86% | |================================================================= | 93% | |======================================================================| 100%mnp_ac$toptables$condition <- "AC"
mnp_ac$toptables$cc <- "MNP cells"T cells
sct_counts <- GetAssayData(tcell, assay = "RNA", layer = "counts")
metadata_aa <- tcell@meta.data %>%
dplyr::filter(group == "AA",
condition != "Dil")
metadata_aa$group_id <- droplevels(metadata_aa$condition)
metadata_aa$sample_id <- droplevels(metadata_aa$Channel)
tcell_aa = sc_diffexp(sct_counts, metadata_aa, cell_ann_col = "annotation")## | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100%tcell_aa$toptables$condition <- "AA"
tcell_aa$toptables$cc <- "T cells"
metadata_ac <- tcell@meta.data %>%
dplyr::filter(group == "AC",
condition != "Dil")
metadata_ac$group_id <- droplevels(metadata_ac$condition)
metadata_ac$sample_id <- droplevels(metadata_ac$Channel)
tcell_ac = sc_diffexp(sct_counts, metadata_ac, cell_ann_col = "annotation")## | | | 0% | |======= | 10% | |============== | 20% | |===================== | 30% | |============================ | 40% | |=================================== | 50% | |========================================== | 60% | |================================================= | 70% | |======================================================== | 80% | |=============================================================== | 90% | |======================================================================| 100%tcell_ac$toptables$condition <- "AC"
tcell_ac$toptables$cc <- "T cells"All cells
fdr_cutoff <- 0.2
diffexp <- rbind(all_ac$toptables, all_aa$toptables) %>%
dplyr::rename(cell = cluster_id,
adj.P.Val = p_adj.loc)
logfc = diffexp %>%
dplyr::select(gene, cell, logFC, cc, condition) %>%
spread(gene, logFC, fill = 0)
logfc2 <- as.matrix(logfc[, -c(1:4)])
rownames(logfc2) <- logfc$cell
cctype <- logfc$cc
cctype[logfc$cc == "All cells"] <- logfc$cell[logfc$cc == "All cells"]
cctype[cctype == "CD4 T cells"] <- "T cells"
cctype[cctype == "CD8 T cells"] <- "T cells"
cctype[cctype == "NK cells"] <- "T cells"
cctype[cctype == "MNP"] <- "MNP cells"
fdr = diffexp %>%
dplyr::select(gene, cell, adj.P.Val, cc, condition) %>%
spread(gene, adj.P.Val, fill = 1)
fdr2 <- fdr[, -c(1:4)]
row_ha <- rowAnnotation(cells=cctype, col = list(
cells = c("B cells"="#999999", "Epithelial cells"="#009E73",
"Mast cells"="#F0E442", "MNP cells"="#D55E00", "T cells"="#56B4E9")
))
logfc$condition[logfc$condition == "AA"] <- "Allergic Asthma"
logfc$condition[logfc$condition == "AC"] <- "Allergic Controls"
ht_allcells <- Heatmap(logfc2, right_annotation = row_ha,
row_split = logfc$condition,
cluster_rows = FALSE,
border = TRUE, name="logFC",
cell_fun = function(j, i, x, y, width, height, fill) {
if(fdr2[i, j] < fdr_cutoff)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
ht_allcells
ht_allcells <- grid.grabExpr(draw(ht_allcells, merge_legends = FALSE))
ggsave(here("results", "all_cells_heatmap.pdf"),
plot = ht_allcells, width = 18, height = 5)
## ncells
ncells_all <- data@meta.data %>%
dplyr::group_by(group, condition, cluster) %>%
summarise(Freq = n()) %>%
mutate(group_condition = paste(group, condition, sep="_")) %>%
dplyr::filter(condition != "Dil") %>%
ungroup() %>%
dplyr::select(-c("group", "condition")) %>%
spread(group_condition, Freq) %>%
dplyr::rename(annotation = "cluster")
p1 <- scale(ncells_all[,-1], center = FALSE,
scale = colSums(ncells_all[,-1])) %>%
as.data.frame() %>%
mutate(annotation = as.character(ncells_all$annotation)) %>%
gather(cond, prop, -annotation) %>%
mutate(cond = factor(cond, c("AC_Bln", "AC_Ag", "AA_Bln", "AA_Ag"))) %>%
ggplot(aes(x = cond, y = prop, fill = annotation)) +
geom_bar(stat="identity", color = "black") +
xlab("condition") +
ylab("Cell proportions")
ggsave(here::here("results", "cell_prop_allcells.png"), p1)
p1
## nsig genes
nsig_all <- diffexp %>%
mutate(fc = ifelse(logFC > 0, "up", "down")) %>%
dplyr::group_by(condition, cell, fc) %>%
dplyr::filter(adj.P.Val < fdr_cutoff) %>%
summarise(n = n()) %>%
mutate(cond_fc = paste(condition,fc, sep="_")) %>%
ungroup() %>%
dplyr::select(-c(condition, fc)) %>%
spread(cond_fc, n, fill= 0)
knitr::kable(nsig_all)| cell | AA_down | AA_up | AC_down | AC_up |
|---|---|---|---|---|
| B cells | 6 | 26 | 1 | 0 |
| CD4 T cells | 10 | 44 | 2 | 2 |
| CD8 T cells | 10 | 56 | 2 | 1 |
| Epithelial cells | 3 | 26 | 0 | 0 |
| Mast cells | 2 | 6 | 2 | 6 |
| MNP | 7 | 36 | 3 | 3 |
| NK cells | 3 | 17 | 0 | 0 |
MNP cells
diffexp <- rbind(mnp_aa$toptables, mnp_ac$toptables) %>%
dplyr::rename(cell = cluster_id,
adj.P.Val = p_adj.loc)
logfc = diffexp %>%
dplyr::select(gene, cell, logFC, cc, condition) %>%
spread(gene, logFC, fill = 0)
logfc2 <- as.matrix(logfc[, -c(1:4)])
rownames(logfc2) <- logfc$cell
cctype <- logfc$cell
fdr = diffexp %>%
dplyr::select(gene, cell, adj.P.Val, cc, condition) %>%
spread(gene, adj.P.Val, fill = 1)
fdr2 <- fdr[, -c(1:4)]
row_ha <- rowAnnotation(cells=cctype)
logfc$condition[logfc$condition == "AA"] <- "Allergic Asthma"
logfc$condition[logfc$condition == "AC"] <- "Allergic Controls"
mnp_cells <- Heatmap(logfc2, right_annotation = row_ha,
row_split = logfc$condition,
border = TRUE, name="logFC",
cell_fun = function(j, i, x, y, width, height, fill) {
if(fdr2[i, j] < fdr_cutoff)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
mnp_cells
mnp_cells <- grid.grabExpr(draw(mnp_cells, merge_legends = FALSE))
ggsave(here::here("results", "mnp_cells_heatmap.pdf"),
plot = mnp_cells, width = 18, height = 5)
## ncells
ncells_mnp <- mnp@meta.data %>%
dplyr::group_by(group, condition, annotation) %>%
summarise(Freq = n()) %>%
mutate(group_condition = paste(group, condition, sep="_")) %>%
dplyr::filter(condition != "Dil") %>%
ungroup() %>%
dplyr::select(-c("group", "condition")) %>%
spread(group_condition, Freq)
p2 <- scale(ncells_mnp[,-1], center = FALSE,
scale = colSums(ncells_mnp[,-1])) %>%
as.data.frame() %>%
mutate(annotation = as.character(ncells_mnp$annotation)) %>%
gather(cond, prop, -annotation) %>%
mutate(cond = factor(cond, c("AC_Bln", "AC_Ag", "AA_Bln", "AA_Ag"))) %>%
ggplot(aes(x = cond, y = prop, fill = annotation)) +
geom_bar(stat="identity", color = "black") +
xlab("condition") +
ylab("Cell proportions")
ggsave(here::here("results", "cell_prop_mnp.png"), p2, height = 5)
p2
## nsig genes
nsig_mnp <- diffexp %>%
mutate(fc = ifelse(logFC > 0, "up", "down")) %>%
dplyr::group_by(condition, cell, fc) %>%
dplyr::filter(adj.P.Val < fdr_cutoff) %>%
summarise(n = n()) %>%
mutate(cond_fc = paste(condition,fc, sep="_")) %>%
ungroup() %>%
dplyr::select(-c(condition, fc)) %>%
spread(cond_fc, n, fill= 0)
knitr::kable(nsig_mnp)| cell | AA_down | AA_up | AC_down | AC_up |
|---|---|---|---|---|
| DC2 (CD1C) | 0 | 0 | 0 | 1 |
| Mac1 (FABP4) | 0 | 0 | 5 | 1 |
| Mac2 (A2M) | 2 | 2 | 0 | 0 |
| MC2 (SPP1) | 5 | 7 | 0 | 6 |
| MC3 (AREG) | 1 | 0 | 0 | 0 |
| migDC (CCR7) | 1 | 0 | 0 | 0 |
| quiesMC | 0 | 4 | 2 | 8 |
T cells
diffexp <- rbind(tcell_ac$toptables, tcell_aa$toptables) %>%
dplyr::rename(cell = cluster_id,
adj.P.Val = p_adj.loc)
logfc = diffexp %>%
dplyr::select(gene, cell, logFC, cc, condition) %>%
spread(gene, logFC, fill = 0)
logfc2 <- as.matrix(logfc[, -c(1:4)])
rownames(logfc2) <- logfc$cell
cctype <- logfc$cell
fdr = diffexp %>%
dplyr::select(gene, cell, adj.P.Val, cc, condition) %>%
spread(gene, adj.P.Val, fill = 1)
fdr2 <- fdr[, -c(1:4)]
# fdr2[is.na(fdr2)] <- 1
row_ha <- rowAnnotation(cells=cctype)
logfc$condition[logfc$condition == "AA"] <- "Allergic Asthma"
logfc$condition[logfc$condition == "AC"] <- "Allergic Controls"
t_cells <- Heatmap(logfc2, right_annotation = row_ha,
row_split = logfc$condition,
border = TRUE, name="logFC",
cell_fun = function(j, i, x, y, width, height, fill) {
if(fdr2[i, j] < fdr_cutoff)
grid.text("*", x, y, gp = gpar(fontsize = 20))
})
t_cells
t_cells <- grid.grabExpr(draw(t_cells, merge_legends = FALSE))
ggsave(here::here("results", "t_cells_heatmap.pdf"),
plot = t_cells, width = 18, height = 5)
## ncells
ncells_tcells <- tcell@meta.data %>%
dplyr::group_by(group, condition, annotation) %>%
summarise(Freq = n()) %>%
mutate(group_condition = paste(group, condition, sep="_")) %>%
dplyr::filter(condition != "Dil") %>%
ungroup() %>%
dplyr::select(-c("group", "condition")) %>%
spread(group_condition, Freq)
p3 <- scale(ncells_tcells[,-1], center = FALSE,
scale = colSums(ncells_tcells[,-1])) %>%
as.data.frame() %>%
mutate(annotation = as.character(ncells_tcells$annotation)) %>%
gather(cond, prop, -annotation) %>%
mutate(cond = factor(cond, c("AC_Bln", "AC_Ag", "AA_Bln", "AA_Ag"))) %>%
ggplot(aes(x = cond, y = prop, fill = annotation)) +
geom_bar(stat="identity", color = "black") +
xlab("condition") +
ylab("Cell proportions")
ggsave(here::here("results", "cell_prop_tcells.png"), p3, height = 5)
p4 <- cowplot::plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 1)
ggsave(here::here("results", "cell_prop_combined.png"), p4, height = 12)
p4
## nsig genes
nsig_tcells <- diffexp %>%
mutate(fc = ifelse(logFC > 0, "up", "down")) %>%
dplyr::group_by(condition, cell, fc) %>%
dplyr::filter(adj.P.Val < fdr_cutoff) %>%
summarise(n = n()) %>%
mutate(cond_fc = paste(condition,fc, sep="_")) %>%
ungroup() %>%
dplyr::select(-c(condition, fc)) %>%
spread(cond_fc, n, fill= 0)
knitr::kable(nsig_tcells)| cell | AA_down | AA_up | AC_down | AC_up |
|---|---|---|---|---|
| CD4 T (CD40LG) | 8 | 33 | 0 | 0 |
| CD4 Th17 (RORA) | 5 | 37 | 2 | 2 |
| CD4 Th2 (GATA3) | 1 | 6 | 0 | 0 |
| CD4 Treg (FOXP3) | 0 | 0 | 1 | 0 |
| CD8 T (CLIC3) | 4 | 40 | 5 | 11 |
| CD8 T (GZMK) | 6 | 13 | 0 | 2 |
| quiesCD8 T | 4 | 9 | 0 | 0 |
| Tgd (TRDC) | 8 | 19 | 1 | 4 |
number of cells
ncells <- cbind(cellclass = c(as.character(ncells_all$annotation), rep("MNP", nrow(ncells_mnp)), rep("T cells", nrow(ncells_tcells))),
rbind(ncells_all, ncells_mnp, ncells_tcells))
write.csv(ncells[, c("cellclass", "annotation", "AC_Bln", "AC_Ag", "AA_Bln", "AA_Ag")], here::here("results", "ncells.csv"))
knitr::kable(ncells)| cellclass | annotation | AA_Ag | AA_Bln | AC_Ag | AC_Bln |
|---|---|---|---|---|---|
| B cells | B cells | 582 | 434 | 184 | 173 |
| CD4 T cells | CD4 T cells | 2598 | 531 | 579 | 1180 |
| CD8 T cells | CD8 T cells | 1783 | 1993 | 1748 | 3583 |
| Epithelial cells | Epithelial cells | 2387 | 5028 | 1616 | 7720 |
| MNP | MNP | 3213 | 499 | 2112 | 946 |
| Mast cells | Mast cells | 276 | 125 | 102 | 40 |
| NK cells | NK cells | 189 | 60 | 93 | 94 |
| MNP | AS DC (AXL) | 45 | 3 | 29 | 8 |
| MNP | Cycling (PCLAF) | 3 | 16 | 8 | 18 |
| MNP | DC1 (CLEC9A) | 9 | 2 | 8 | 7 |
| MNP | DC2 (CD1C) | 557 | 24 | 183 | 43 |
| MNP | MC1 (CXCL10) | 22 | 7 | 56 | 18 |
| MNP | MC2 (SPP1) | 174 | 25 | 414 | 91 |
| MNP | MC3 (AREG) | 188 | 34 | 373 | 76 |
| MNP | MC4 (CCR2) | 895 | 14 | 326 | 20 |
| MNP | Mac1 (FABP4) | 15 | 129 | 67 | 293 |
| MNP | Mac2 (A2M) | 102 | 20 | 50 | 51 |
| MNP | migDC (CCR7) | 100 | 21 | 77 | 30 |
| MNP | pDC (TCF4) | 447 | 30 | 269 | 35 |
| MNP | quiesMC | 541 | 148 | 210 | 159 |
| MNP | quiesMac | 12 | 10 | 18 | 49 |
| T cells | CD4 T (CD40LG) | 1570 | 330 | 369 | 873 |
| T cells | CD4 Th2 (GATA3) | 199 | 33 | 33 | 23 |
| T cells | CD4 Th17 (RORA) | 344 | 241 | 177 | 441 |
| T cells | CD4 ThIFNR (ISG15) | 47 | 10 | 15 | 20 |
| T cells | CD4 Treg (FOXP3) | 579 | 43 | 86 | 122 |
| T cells | CD8 T (CLIC3) | 699 | 658 | 817 | 1403 |
| T cells | CD8 T (EGR2) | 19 | 21 | 14 | 44 |
| T cells | CD8 T (GZMK) | 154 | 82 | 160 | 306 |
| T cells | Tgd (TRDC) | 360 | 305 | 245 | 295 |
| T cells | quiesCD8 T | 377 | 545 | 388 | 956 |
number of significant gene-transcripts
nsig <- cbind(cellclass = c(as.character(nsig_all$cell),
rep("MNP", nrow(nsig_mnp)),
rep("T cells", nrow(nsig_tcells))),
rbind(nsig_all, nsig_mnp, nsig_tcells))
write.csv(nsig[, c("cellclass", "cell", "AC_up", "AC_down","AA_up", "AA_down")], here::here("results", "nsig.csv"))
knitr::kable(nsig)| cellclass | cell | AA_down | AA_up | AC_down | AC_up |
|---|---|---|---|---|---|
| B cells | B cells | 6 | 26 | 1 | 0 |
| CD4 T cells | CD4 T cells | 10 | 44 | 2 | 2 |
| CD8 T cells | CD8 T cells | 10 | 56 | 2 | 1 |
| Epithelial cells | Epithelial cells | 3 | 26 | 0 | 0 |
| Mast cells | Mast cells | 2 | 6 | 2 | 6 |
| MNP | MNP | 7 | 36 | 3 | 3 |
| NK cells | NK cells | 3 | 17 | 0 | 0 |
| MNP | DC2 (CD1C) | 0 | 0 | 0 | 1 |
| MNP | Mac1 (FABP4) | 0 | 0 | 5 | 1 |
| MNP | Mac2 (A2M) | 2 | 2 | 0 | 0 |
| MNP | MC2 (SPP1) | 5 | 7 | 0 | 6 |
| MNP | MC3 (AREG) | 1 | 0 | 0 | 0 |
| MNP | migDC (CCR7) | 1 | 0 | 0 | 0 |
| MNP | quiesMC | 0 | 4 | 2 | 8 |
| T cells | CD4 T (CD40LG) | 8 | 33 | 0 | 0 |
| T cells | CD4 Th17 (RORA) | 5 | 37 | 2 | 2 |
| T cells | CD4 Th2 (GATA3) | 1 | 6 | 0 | 0 |
| T cells | CD4 Treg (FOXP3) | 0 | 0 | 1 | 0 |
| T cells | CD8 T (CLIC3) | 4 | 40 | 5 | 11 |
| T cells | CD8 T (GZMK) | 6 | 13 | 0 | 2 |
| T cells | quiesCD8 T | 4 | 9 | 0 | 0 |
| T cells | Tgd (TRDC) | 8 | 19 | 1 | 4 |
sessionInfo
sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: x86_64-apple-darwin20
## Running under: macOS Ventura 13.6.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Vancouver
## tzcode source: internal
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] here_1.0.2 circlize_0.4.16
## [3] ComplexHeatmap_2.26.0 limma_3.66.0
## [5] scater_1.38.0 scuttle_1.20.0
## [7] muscat_1.24.0 SingleCellExperiment_1.32.0
## [9] SummarizedExperiment_1.40.0 Biobase_2.70.0
## [11] GenomicRanges_1.62.0 Seqinfo_1.0.0
## [13] IRanges_2.44.0 S4Vectors_0.48.0
## [15] BiocGenerics_0.56.0 generics_0.1.4
## [17] MatrixGenerics_1.22.0 matrixStats_1.5.0
## [19] lubridate_1.9.4 forcats_1.0.1
## [21] stringr_1.6.0 dplyr_1.1.4
## [23] purrr_1.1.0 readr_2.1.6
## [25] tidyr_1.3.1 tibble_3.3.0
## [27] ggplot2_4.0.1 tidyverse_2.0.0
## [29] Seurat_5.3.1 SeuratObject_5.2.0
## [31] sp_2.2-0 anndata_0.8.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 spatstat.sparse_3.1-0 httr_1.4.7
## [4] RColorBrewer_1.1-3 doParallel_1.0.17 numDeriv_2016.8-1.1
## [7] backports_1.5.0 tools_4.5.1 sctransform_0.4.2
## [10] R6_2.6.1 lazyeval_0.2.2 uwot_0.2.4
## [13] mgcv_1.9-3 GetoptLong_1.0.5 withr_3.0.2
## [16] prettyunits_1.2.0 gridExtra_2.3 fdrtool_1.2.18
## [19] progressr_0.18.0 textshaping_1.0.4 cli_3.6.5
## [22] Cairo_1.7-0 spatstat.explore_3.5-3 fastDummies_1.7.5
## [25] sandwich_3.1-1 labeling_0.4.3 slam_0.1-55
## [28] sass_0.4.10 mvtnorm_1.3-3 S7_0.2.0
## [31] spatstat.data_3.1-9 blme_1.0-6 ggridges_0.5.7
## [34] pbapply_1.7-4 systemfonts_1.3.1 parallelly_1.45.1
## [37] rstudioapi_0.17.1 shape_1.4.6.1 gtools_3.9.5
## [40] ica_1.0-3 spatstat.random_3.4-2 Matrix_1.7-3
## [43] ggbeeswarm_0.7.2 abind_1.4-8 lifecycle_1.0.4
## [46] yaml_2.3.12 edgeR_4.8.0 gplots_3.2.0
## [49] SparseArray_1.10.1 Rtsne_0.17 promises_1.5.0
## [52] crayon_1.5.3 miniUI_0.1.2 lattice_0.22-7
## [55] beachmat_2.26.0 cowplot_1.2.0 magick_2.9.0
## [58] pillar_1.11.1 knitr_1.50 rjson_0.2.23
## [61] boot_1.3-31 estimability_1.5.1 corpcor_1.6.10
## [64] future.apply_1.20.0 codetools_0.2-20 glue_1.8.0
## [67] spatstat.univar_3.1-4 data.table_1.17.8 vctrs_0.6.5
## [70] png_0.1-8 spam_2.11-1 Rdpack_2.6.4
## [73] gtable_0.3.6 assertthat_0.2.1 cachem_1.1.0
## [76] xfun_0.54 rbibutils_2.4 S4Arrays_1.10.0
## [79] mime_0.13 coda_0.19-4.1 reformulas_0.4.2
## [82] survival_3.8-3 iterators_1.0.14 statmod_1.5.1
## [85] fitdistrplus_1.2-4 ROCR_1.0-11 nlme_3.1-168
## [88] pbkrtest_0.5.5 EnvStats_3.1.0 progress_1.2.3
## [91] RcppAnnoy_0.0.22 rprojroot_2.1.1 bslib_0.9.0
## [94] TMB_1.9.18 irlba_2.3.5.1 vipor_0.4.7
## [97] KernSmooth_2.23-26 otel_0.2.0 colorspace_2.1-2
## [100] ggrastr_1.0.2 DESeq2_1.50.0 tidyselect_1.2.1
## [103] emmeans_2.0.1 compiler_4.5.1 BiocNeighbors_2.4.0
## [106] DelayedArray_0.36.0 plotly_4.11.0 caTools_1.18.3
## [109] scales_1.4.0 remaCor_0.0.20 lmtest_0.9-40
## [112] digest_0.6.37 goftest_1.2-3 presto_1.0.0
## [115] spatstat.utils_3.2-0 minqa_1.2.8 variancePartition_1.40.0
## [118] rmarkdown_2.30 aod_1.3.3 RhpcBLASctl_0.23-42
## [121] XVector_0.50.0 htmltools_0.5.8.1 pkgconfig_2.0.3
## [124] lme4_1.1-37 lpsymphony_1.38.0 fastmap_1.2.0
## [127] rlang_1.1.6 GlobalOptions_0.1.2 htmlwidgets_1.6.4
## [130] shiny_1.11.1 farver_2.1.2 jquerylib_0.1.4
## [133] IHW_1.38.0 zoo_1.8-14 jsonlite_2.0.0
## [136] BiocParallel_1.44.0 BiocSingular_1.26.0 magrittr_2.0.4
## [139] dotCall64_1.2 patchwork_1.3.2 Rcpp_1.1.0
## [142] viridis_0.6.5 reticulate_1.44.0 stringi_1.8.7
## [145] MASS_7.3-65 plyr_1.8.9 parallel_4.5.1
## [148] listenv_0.10.0 ggrepel_0.9.6 deldir_2.0-4
## [151] splines_4.5.1 tensor_1.5.1 hms_1.1.4
## [154] locfit_1.5-9.12 igraph_2.2.1 spatstat.geom_3.6-0
## [157] RcppHNSW_0.6.0 reshape2_1.4.4 ScaledMatrix_1.18.0
## [160] evaluate_1.0.5 nloptr_2.2.1 tzdb_0.5.0
## [163] foreach_1.5.2 httpuv_1.6.16 RANN_2.6.2
## [166] polyclip_1.10-7 future_1.67.0 clue_0.3-66
## [169] scattermore_1.2 rsvd_1.0.5 broom_1.0.10
## [172] xtable_1.8-4 fANCOVA_0.6-1 RSpectra_0.16-2
## [175] later_1.4.4 ragg_1.5.0 viridisLite_0.4.2
## [178] lmerTest_3.1-3 glmmTMB_1.1.13 beeswarm_0.4.0
## [181] cluster_2.1.8.1 timechange_0.3.0 globals_0.18.0